{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_csv('/Users/baconbaker/Documents/Studium/ANM/anm-project/data/test_data/esb.csv')\n",
    "train_data = pd.read_csv('/Users/baconbaker/Documents/Studium/ANM/anm-project/data/train_data/esb.csv')\n",
    "\n",
    "train_data = one_hot(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot(df):\n",
    "    df['start_time'] = pd.to_datetime(df['startTime'], unit='ms', origin='unix')\n",
    "    df = df.sort_values(by='start_time')\n",
    "    df = df.set_index('start_time')\n",
    "    return df\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serviceName</th>\n",
       "      <th>startTime</th>\n",
       "      <th>avg_time</th>\n",
       "      <th>num</th>\n",
       "      <th>succee_num</th>\n",
       "      <th>succee_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-03 16:00:00</th>\n",
       "      <td>osb_001</td>\n",
       "      <td>1588521600000</td>\n",
       "      <td>0.5691</td>\n",
       "      <td>347</td>\n",
       "      <td>347</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-03 16:01:00</th>\n",
       "      <td>osb_001</td>\n",
       "      <td>1588521660000</td>\n",
       "      <td>0.5813</td>\n",
       "      <td>354</td>\n",
       "      <td>354</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-03 16:02:00</th>\n",
       "      <td>osb_001</td>\n",
       "      <td>1588521720000</td>\n",
       "      <td>0.5397</td>\n",
       "      <td>363</td>\n",
       "      <td>363</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-03 16:03:00</th>\n",
       "      <td>osb_001</td>\n",
       "      <td>1588521780000</td>\n",
       "      <td>0.6190</td>\n",
       "      <td>387</td>\n",
       "      <td>387</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-03 16:04:00</th>\n",
       "      <td>osb_001</td>\n",
       "      <td>1588521840000</td>\n",
       "      <td>0.4909</td>\n",
       "      <td>387</td>\n",
       "      <td>387</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    serviceName      startTime  avg_time  num  succee_num  \\\n",
       "start_time                                                                  \n",
       "2020-05-03 16:00:00     osb_001  1588521600000    0.5691  347         347   \n",
       "2020-05-03 16:01:00     osb_001  1588521660000    0.5813  354         354   \n",
       "2020-05-03 16:02:00     osb_001  1588521720000    0.5397  363         363   \n",
       "2020-05-03 16:03:00     osb_001  1588521780000    0.6190  387         387   \n",
       "2020-05-03 16:04:00     osb_001  1588521840000    0.4909  387         387   \n",
       "\n",
       "                     succee_rate  \n",
       "start_time                        \n",
       "2020-05-03 16:00:00          1.0  \n",
       "2020-05-03 16:01:00          1.0  \n",
       "2020-05-03 16:02:00          1.0  \n",
       "2020-05-03 16:03:00          1.0  \n",
       "2020-05-03 16:04:00          1.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_thresh(data):\n",
    "    ub = np.percentile(data, 99)\n",
    "    lb = np.percentile(data, .1)\n",
    "    return ub, lb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(651.0, 311.884)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_thresh(train_data['num'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='index', ylabel='num'>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OyHCxDXC/mVfRPPG443/5Ar7/j8VGGK/i3BOKDBHGD+3r+vtZU0YAsHrqxRKxjAGHTxiMqWOaLce5qWixNIHIu3KLJSwF3dbRYziFAS1LtlBknh+Lz0611n3j7d1utX95xlhkqDJ0Rhyce1yKjyuP+nwGvUWGfvzjL+zz5sfbMO26Z3Do9c+GUhrM9jcKbN3VjZ0RVq6UQn8Isho7BSEUUuaTKBaZMRLlyl0cGfHnbDmGicvaypqtpRPoeosMy1t3GUl32jWVck8kchnyzNbklIpY7rYhl8Vh+jBwzCDt2IytxAARScsOiOULROuCN9pSNdjdMPnap3HCr1401nd09hrRMm6w/8YbeT6bMYYbD106E9edfQAyRBXl3EVU2rKOjXPXFVddNoNikWFAY9513407urBhe3D/i0HLRHiLU697BofbAhKiBm+Jqza3o1hkeGDBGnysW8acssxlM9I+VWDMGP1wRSu2qyaZ5S58/nif+/uCNUZItBtkFExHd3UNFDXNnk9kM+SryJJIy9Tns/j3I1pwwr57YKxeI8SuQy2OTP3vj//vPcMzD1gbIF+OKpb8theX4wvTRzs+OiLyWasNwJVehpzTDxJRRbhqGee+eVc3hvStj/5iOmLj3HXFQ6QpFC3voMPVIRdmekautKLm3HdU2HLnzfTGJ5fiV8+8j54CwxETB+Pur84QMlTJ8uHPZgiFIsM+e/YzAgQ4rSO2K1m9fvHjVwgQNymboCeqip5+oSx3n/Ab7SLSMmMGNYGIDMUOOK1gUakSEbbs6rYodsDaAHvKUO5uw8I3P97qabnbfxIVuf2bwPeN2qrm1JQ4OcKqzaWLs5V1zZgsd654GNPoN265u73yt9dsw4JVzgqmXuAsV2X8I9FYqC9+0IrlrdZ4dvFDxrNDh/StR6HIjOqrWSLLJ2vvYf3w7JVH4+QDhgemZcTnE8SpLytLsL6to6p+HKXcfSKrm9h///rhOP+wMa77ccu9PpfB0H5Oq9I+XLSrVJkCLhQZdnX1ordQNKy3MMr9k23y4XuxBC1jl5lf2mrtaxt554va6uUda/UWM1JhTcCohaCI26HKoPkavGgZAPjG3xbinFudFUz9oBJ3ePVDiyI5z4V/eR3H/1KkEOVx4qMHNuGnTywxMsUZmEUpjx3chIl7aP4ybrl3S2gZMdKNQ/zAB+lzMofv9x95F49LyhFXCkq5+wS3yA9pGYQLZ7a47sc5d5kVAEhoGZvilDWKQpFh/x8+hcvvWWg02jBWpX1mIY6O7kKg6JsDRvYHoEX6OKNltL9RK0aZNVjp0LK4HKr8vrSJVID+jdGzp5XMUJXVUw+CnV3anMQituzqxoE/ehpzP9osPeYBYaYlxsz+8ZOzD8CNnzPLVvPZtGSW+9B+9Vh5w2mWCUzEpxPEcndrm6+vMEdYbh+rqKCUu0+ISthNcQMm7dKvQW5tOR2qsMTCy3g5vu3Jd9cb28JY7lt3WRvT907bF4AWMRNk3tNvHDcJj15+BA4YOcDYxnUEH7lEabkzxqQTolR6cuK4M1QZNCXlN7u5WGS+E2UqGQo5RDJiDYI5SzdalCAAzxLHDMzy8Wcw72v80D6Wwn91WatyFxV2X0lIstVy929MuFGgd81dhcVr23DGzS/jwB89rSVEVQhKufuE+JL7uyhuwGwsfSWed0AeLSPCb/ngMNEyW9utcdF8uN/RXbA4gkshmyEcNKoZALD/CE3BNzdp5+K3EyVffe8bq/HzJ993bA8bMeQXcUfLMKaNWLIZwvdO2xcPXToTv/jcQZg8ull63IV3vI5J3/2nr2uY0TLR3+Oe/RtK7+QBmUQ9gjPz+H32sO7PrL8zZjqK7c7mXDaDbIYM5SsaCLzPin3SwrkL1xjnkfMCeJeHuOWFZVi0tg0A8GYFCwMq5e4TooPEqwgYH2r5p2XMZSL/iQ5hLHd7bQte2re7UAxkuYv43un74sFLDsfEPTS+kn+8otQZj73ziXR7pS33+Dh3nZaBNnrIEOGrnxqPaWMG4tzpo3HONMd88wDgWnpaBjNaJjrwmkT9G/P4z78tDE07yJ77TiH00B61xmDtnwzMuDGZzVKfywjRMuZxfeqc+QSiLKKV/+Oz9ve8h3s85h14X6g9s/iTNs/zlAOl3H1CDG3iX3aZY3WCnuh0/mFjpeexW+72db/zqhaKDO9+0hZoRh43yx1AIMtdRH0ui4PHDjLPUwHL3S1Ms9JztcYdCgl98nJ7pFYpB6sfVCLOnfulnnlvAx59+xNjYpEosF34UNjrHdnvocjMdyeLd6/PZYyPgfiOeR/I2Cz3ucs341fPfGBpb+I+srLYXvcuBgK4BTlEARXn7hP2lGM+G8/dtgL9I5obPWfqkTkuhWBIKbcsQ7HIcNpvX0ZDPoOl153i6xh74oWoJHIhlbsdZrRMNFpj6frtrhZppUu8x0fLmJZ7ocgcHzcvWhDQqJxc1ttuY7a/UcDucAw6F4AXtgs10uvzNuVuvwvGTFpGarlnpZY7h9gViozhvD+9BgD4tFB7Rjxv3/ocunr9l4LgfH9TXTZUAppfKMvdBjd2IqoEBGdcuHVDp0/LnTcQP3UuOOydT6SOwlrudpiceySnwxsr3TnJytMyFT29K7oEzl3LHrb+fsTEIZ7Wux8DwXSoRneT9pGUn6Q/v7Ba7uZ563IZxxdKdKjKWnVdLmNmqBYYjpw4BI9efoTxu4VzF44TR75iv230MVeDDKMHNinlXk24OUL90iWl4KBlbG/A70dE9LK/b6sf7QZRGV5w+FhLdmcZRSYtMO4voM5Y+PFWbGt3Wj+NHgqimg7Vcgq1BYUZLcP0uj+2ssu5DG45f5rr8e1dfpR7eTLKYLeCo5pftLdQxEsfmKM30XLPkLOpMZNyl9Iy2QwZo74iA0Y0NxgBAtox5r5iGxAn8rEmH/q/FxGjBzVhfVtnxXw7Srnb4DqtWURFf7wzVK0ZmF5YtMZ0xJx000u+jikwhiF963HzeVNx7Zn7I58lYwgalfIKw7kXiwyfueVVTPnxMwC0SIPrH38Pbe09ns7rSnPu4i30CWmdhUGnEYOtrcuc3Xt4hBu2+2hDZpx7YPEMLFm3XYjsYY7RWnuAUgQfbthhFB2zy/TLZz7Ay8sE5S5Y7gRyKEdNFndahiBOVsIcETX23DzeN1ZtNguGiaOpsDXtRg9qRFdvEds7KlOyQSl3G3qLDP9x9Hgs+fHJlu2y5KIwsDc2si2LFRu9cP+C1YGvXSwyNOQzOGPyCKNgGR86e9WWCQIzzl2uNdo6enD94+9ZSgeIFt5HrTvxmVtewZ/+tQLXPf6ep+Ve+Th38/xueQuVAFeYPK5aRp/v4RFu6CecVlRuYfDOmm045Tf/wlUPvgNA/qH90f+953vE+5lbXsUdL6+Q/jbPlrgkcvmy+QOY8Z9L1VWyfty8qNIiA5okbZCIMOfbx6BlcFPoiqW8mOD6ClEzSrnbUCgy5DMZB48WFedeqiqkGP3ipW/DWFyy6o9cuQedKcoN/F7c9O4tLyzDn/61As+8Z04qLvoCjvvli3j3k+0AtJrlducZAHztU+MAVIOW0f5OGd2M8UO945qjAmPMUO78udhpGQDGHAAytHcX8MbKLTjk+mfxUat8rt9yLfczf/cKAOANPdnILXtz7bbS5XELRYadXb2uSs4ewity+bJWa4lzl3QiAowHwFBKuTujlfg+44b0wWHjBrvGtB82bpB0O8fogUq5VwVX3v8Wjrjhedfa5l7zhgZBKQs57PR5fjITC0XmGOI36FZQZJZ7ifIDfAQkpmf3uHSO7Z09jhmiAOBsvb58xR2quga4d9YMI7Ox0ugtmvRGwVDuzv3sSuvCw83Q213dvbjmoUVo3dFlme9TRNGw3KOBG0XmZyTKlWObHhFjH018tMlaIM4RCmk7X5GZtWVkbCMRmXH+DLB/IsRHyyOW7ODnzWTIkkAl4pCWQUapDhlG65Z7pZyqSrkDuP3lFXjozbWGk9IeFrjw+yfi2jO9kxb8wt5RxaE/kTXuNYhV5Wdkoc2VKrfco+LcSxUO4x1nZ1cv1rd1YuOOToujSsT2jh7pRyKbIWQzVPGp90wFIa+5XwmIWZCc4+3j4uT/13eONZYH9TE5+I7ugkB1yeWOKs6dH+72LjbsKK24uKx8CstSMtkrqcr2N+LcJfdPlmswT6q0WGTS83MZMgTXCUr61Ofw2Dc+Jb8JaJz7RUe0GLkxUcNXnDsRrQSwA0ABQC9jbDoRDQJwH4AWACsBfJ4xtpW0XvAbAKcCaAfwFcbYm9GLHg1ad3Thusfes2zL6sOwBy+Zia7eAgYKhYTsOGPyCIwa2Oj7enYlIVoFPQXmOXVaPutuJXR2F0rGP8ss93qu3Esor79//XBflrJhuUtswraOHtzxykoA2sTFM37mPbFDW0ev1LLMEukRDxWmZfT7zZDcAqwEZPd0xuQR0n0H9zXbpUgb3TPvY+PD4MYHG8o9ItvdzXJf31bacuejzrYOfxmtnT0F9KnLYld3QVPUtntgzBw5Sh2qZP242XcRPx69RSatUc938TKKSj3bprocfnhGNEajDEEs92MZY1MYY9P19dkAnmOMTQLwnL4OAKcAmKT/mwXg1qiEjRK9hSL+8vIKabo/t9wPHjsQMycM8TzPzedNxVUn7+P7unb6Q+wUXLF/ZWYL/vDlgx3H2qmBA0cOwPSxAwH4s9wLRWc8O58KsFSc+yEtg4xZpbyQ8eDcr3rgHWN5vS/6ycVqyhCyRFUrHEZEkdFWpSBTkrK5PQFYKKv9R5jDfzGypBRdF9X30f4R4fL4aZdcRp6oVEqmjp4CXrvmeLz1gxM156jDoWqqVXm0jEDLSPYR3/W9b8jLCHClXq12EQbl0DJnAbhTX74TwNnC9ruYhtcANBPR8DKuExrb2rtx/3xrVMmCVVsxf+UW3D3vY/z4sfdw19yVjuNkDqyoYLeQi4w5LIfj990DJ+3v5PjrbFzjqIGNuPhIzbnol5ax00J89pmwtWXsMJKYJEpqpRAhs9pHLXb7DFAcGW65V3jWMsuMU9Wy3CXPzY0SymcJowY24sZzDsS4IX2kRkZPr1xTRhVbbZSgtr2Lof3qtfl0XT7AbR09+NvrH4Mx5uDcS43IiLTopeamOneHKrfcZbSMxXL3DoVcs0XeTk1axr1hiPXhhw8or5haGPjVYgzA00S0gIhm6duGMcZ44eb1AHhu7kgAokZdo2+zgIhmEdF8Iprf2toaQvTS+M9738J3HnjHMpvLObe+is/dNhdvrd4GQF6nI6pUfBnsbUHWmQfZaCBecdFe+jWbITToUT1+Qs7ktIzWBLIRRct4FQ4Th92rt5SOoshnM4aFdduXzKSdLFHFJuIWISbCxGW5ezVFIsLLVx2HLxwyBkSE/zhqvGMft0QifpUwox/Zh8FuuZeizq577D1c/dAivLFyq4WWefnDTVi6zj0p74oTJuHfjxhnrGucu3YNHvTAIGSoujw/Jvx1cO7Chu5C0TLRPYfhUNX/5iX1f47bR1OJ8645Hk//11Gu91Qp+K0tcyRjbC0R7QHgGSJaKv7IGGNEFKiVMMb+COCPADB9+vSK9NK1+izluySc2cMLtcI+A5qcfHolsxHtSkJ2427FxeyWez6bMeLAQztUI7bc+aBHpnhF5e7mhKrLmkWd8lkzILlZeE+ZjDkvZiXBGDM6ryWCgrGKOVjtzy1IW8xkCI35rKUtuE15Z3Lu/rB4bRt6CkVMHTPQ8tzXb+/En176CIfoYX/1uQy6erUyxRkP6oz3yQ3bO42s8EKR4Uu3z/OU44oT9rKsE5nOXF6G15qh6jxHRnDCyjl3c3lbezf6N+TR2WP1HfD3z89fl82gp1DAyOZGrN3WYcz8BADDyiyBHBa+LHfG2Fr970YADwM4FMAGTrfofzfqu68FMFo4fJS+rergSoLH5MrAG9/1nznA2FZJ5e5obJK2b9+Hy2OPt83qnRnwVypYGgrJLffIMlQ55+68MT/JNWJcu2a5axCl49Ey1Sg/IBt+P7xwrWe97nJg/2AF/Yj0qbeO7twc8NZQwNI4/eaX8ZlbXtVktB10/RNLcPbvtT7G80OyGULOI6KJj0Zve3E5bnhyqXQfEWdPGWGp/8JBMCOMeLE0ayikGy3D79/5oRaP2dbeIx3d8334t5MbXryPetFe5x06BnsNq0yEjIiSljsR9QGQYYzt0Jc/DeDHAB4FcCGAG/S/j+iHPArgciK6F8BhANoE+qaqcOMbRZi8qvlC3bjeKOAsTyrhWG22BFfIdsWczxL66w1vy67SkQaFonsoZNS0Q1ijuj6XxQ5oVp34HsQOmCVvqzAqFJn5oRUf25X3v42PWnfh2yftHfk1g9AyMtiT71xpmTKiZbxGTNpIsEez3D1GV7zd8oS1Uvjy4WMt9V84iMwPSF54WEb5Acm5iKy0jOx3jq3tPWgZ3OTYJ2szYnhb9aJ0p48diFyW8LPPHui6T5TwQ8sMA/Cw3rlyAO5hjD1JRG8AuJ+ILgawCsDn9f2fgBYGuQxaKORFkUvtE2KkQGdPQVqC1IyHNWHnz6KEvTOJCoTDzXK3K+BsRnOo5TJkyUTUysTKJrZmjqQgM8498K1IYcoYTvGK7yiflccwZ7jlXoXyA+bw2/osN+30VyYizDVFBP3o8gknOHoKRWliHn90YQY/XvkF9cJIMJshV7+IzNHpBbcRDMHk+7OC1VwyWkbgpbzi3Ns6ujGgcYBEHhjXAkTlnnGV94FLZkrvoVIoqdwZYx8BmCzZvhnA8ZLtDMBlkUhXBl76oNXC8e7z/Sdx5ERnWKMsHraSlrvj+hIlaG8WvGM6lD4R8tkMxg5uwrKNpnKfcM0TOPfgUfjFudbXVigy1OdcHKoRRQh5hUL6gei8ymbMkDXx3rnlXmlaBsy0nB28bIWoO7viDOoLsRdau+nZD3HTsx9K5hhgwv/BUPAopM/7TlYPV3UtxBewnIfbR47IpGXyehsWo2VktrvdcndGy5jrPQWGJkkoqn2uYDt1GtcsXiJqMkN1xaZduOAvrzs6ihj/y8Ebn/iCKxkt44jJDcC5OxKg9INHDWzC+u1a6VBes+Xvwmzw5v6SOPdc1Ja79jdsJIsYEaR1Um1ZlJpb7tUoHMaVin3OzKgc0I5rGiUH5B/0UmiWBAgATmVqPLoQ78nLcufXyZBGy7i1A7cJpN3g/rzJsNz5iFu8orT8AGyhkLZ97Mc0SEb89nbO31c1S0OXQk0qd7+ZboDQyEXLPcIZZEpB3vbtDh7ZVtOZM7hvHd5Z04ZDrn8WX7trvuu1ikXmqNve4DND1S/4B+jOV1ehZfbj2NnVi8Vr2/Czfy7xdbxouYuPxmK5Gw7VKCR2B5+/FAAuPXYizjvUjBOoVCcu2JRF0BECd1Ta8c/F6yzWJDdqQlnuPpR7kbESlnswh7Rb8xQtd+5QZUzg3F1SVK2WuxX2UYKseJ19hMqPMP4mILmpJpV7EC5W5njJVzCJyQ6tw9mHhdZ9bvrCVJwzbRSmjGm2bOdWHi87sGmn91RfMu6Vc9xR0Qz8NPfpmX1L1m3H6Te/jD+8+JFlv8f/80jp8VbLXe7u4+UAqmG586eSzRBOOcDMxatU3Dtvu3z0GPQ6blz2f933thH+CwiFw0I8QpEO44k6fGTDRw6Mwaj/wxjDL55aig+FImZBJ79xpWUAPLdUC9Tjz4wJmc1ShyrEaJnSPq+GXBbPf+toPHWFGatuKHc+8ieXg2NETSr3IHwXk3zhK+lQtUNrG7bwN9s+44f2wS8/P9kSDQCYnUw2xJVNLiHSDByVstx5rervCCUHROw/wumkAoBzp4/CnnpcMIOcOzUzVCur3O0dXyzgFVWJZDt4LhB/H0GVu9fsRx8LiWNBomXsJQw45/6Lzx2EBy+ZiVvOn4Znrzwat5w/DV88RBvdFPXy0sUiw7b2Hvx+znJ86fZ5YIzhsrvfxJPvrg90X24jJfHxmKGQ3klMlpwFOEMhz5xizbmsz2cwfmhf7C1knNppmSSWIahJ5R7Mctf+iq+m1OTC5YCHqvH4dGkopCQqRtzOrSR+rGyIK3MCySz36OPctb8jmrViaits5Vq98Ny3jsZnp43Cq7OPw6Etg1yTUXiCTKUdqsyW9CVOwVhxWibLlXuw493KJwPAH1/6CL+fswz/9/YngSx3ka9/f/0OYz7TulwGfepzOPXA4chmCKceONwoRFdk5nR2nKPv6i1ieesuPL4oeGS023MQRypGt2XMU+mKnLsMXzpsDG77klnbqSHnNJTstIxS7lVCKd0+fkgf/LceoyybjquStbsnDO2LW8+fhl99XotkkUXj2huyvd0YHwb9Rq880Zq1B8jLKhRkGaoRl/z1U3PDDTmBZ26oy7rOqMNT26sR5y7eh5ggVAmH6oJVW4xkoLC0zOxT9sFYSVw2oCWR/eKp9/GNvy0MxLmLFMpJN72Eb93/NgB5m+GjS57dWywyY2S5rb0HP3n8PccxZ0wegRvPORADXfwFgD8OO5vJGJEwXvdlr+duPzMRWUJyvTl3/SOSIEcqR00q91K0zO1fOQSnHajxp2blP/P3Sg25OU45cLihfKXRMi5JTBzc4csdiqMHNTnCPHl881urtxl1XIpVyFDlp/eaeuzYvYdKt8s+SOZ5BVqmqhmq5roYQy525nvmfYwl6/wl43jhrrmrjGUztyHYOVqG9MFfvnJIyf3E9PtSsJe2eF/nzmUfONGpyakzcWKWF9531pE6a/IIfOGQMZ5TKvqiZfQRHRO0uzzO3VZ+oQR10yCbZs9WZiOBut13bZlUoVSnH9ncaJScNRwiwhuuRpx7VrRwbC2DN6zhAxqwrq3T0bC5VSdarvbiRu3dBfzo0Xfx19c0hbHyhtPk0+zlIp5DVT+PmPZ+8NiBWLBqKwDgp585EOdOH+U47vJjJ1rC+AgAGHPlhDMekRhRQUswM59LP3FqO6GNXfPwIgCQxJIHg1gDiSfDhLEI/RxhzsRU+hm6hT5KLfesadFy6qxUTDuvSy+jEjncaRlxHzLqu/P7ci8/oK8wuRNaPE6W/OiIltF3P3Bkf/T0FvG90/Z1vZdqoSYtd1mnH9a/3vhbl8uYpWmlSUyV/wzzjuEVD/7AJTPxu3+bKql9of0tWJS7aV1MG9OMju4C7nvDWu64WJSFeVWGlhGdcJx/B7SOLPt4fmnGWMu6Mbx2iXrISRyqi9a04Z8h+Fw3MMZcfTG7ugv4ZFvpssV+wCcL39EpKHeDcw/+XgbaYt1lFnHRYrp6w62Jyka4eaG+C6fOukrU4RnSV+ubfT2Vu5vlbm7PZchQ3LIRuXGMpZ67M87dfpzMcreXH+DyNeazeOKbn8LUMQNd76VaqBnlvnlnlzmxsCQAmg/5/99Fh1q2y4ySSjpUObhFJus3vGGNbG7E6QeNcGwncn4YeAPco189Zk4YgvbuXkfkhOZQtV6rUg5VsbCWOHu8zAqae/Vx2NNW75oPnd2iHmR1S8743cu45O7oJv1izF2p3P7yCsy84XnL6OmIG563THBeCp09Bdw1dyWO/sULaJn9OObpk00D4WkZABjYpw6vf/d4TB7dDAC46uS9HWWki/51uyvNKctqFi3aXIbw3rrt+KxecMwNQ/tpyp2PjK451VmX3s8IJpshXXGLRoGcl/GqCineByBvs4ZxaIRCJo+XqRnlfvBPnsUX/jAXgHz2Ga5s+Iuy14aodigk//LLM1RdrBS9GdqtBsBU0n0bcqjPZRwfLcaYnJaJunCYfhrxHYjFrGRWkMySF51e2mmdfohK13O3c+6AGeLJIVqla7d1YM5S/3MTzPrrAvzgkXelv4V1qHLs0a/BOEffhjwmDO0j3c+PU9ptD1kmd0boV5kMeU74/t1T98U+e/Yz2gT3aYxsdjqEXWkZYXuhqGnqokDnuXLu+jLz2IejXtJm7Q7VKkZP+0bNKHcAeHtNGwB5rO/Vp+6LQX3qDIrAbv2K76YaSUxetEypdjJ6kHYP4tCPD70HNdVJFWiRaR3ZQcvwUqUR0zLLW80QSFEeqXKXPG/Dcne5TnUKhzk/tP/8pnXC431/8KRlfVAfbYRYLLKSxcVe+sD9Q5Atg3Pn4Ef2rc/h9+dPw2enjnQoSX+Wu3y77MMjTtZSKqLoSzPG4kkhMaivbrnL5l9wO5e4ubdY1H013nHumtOVR8s4Z2LSjhMsd4nxYca5m+dMGmpKuXPIaJmT9t8Tb37/REO58Fch4+aqUX7AdKjKQrG8j91rWD88/62jccnRE4xt/L6am+qkoVu9xaLUcucfhegyVJ3nEYtZyYa4+Zysc2l/3SY6zlSh/IBmfVq39anPYQ+dRpChvbuAbe3d+Nk/l2D6T541YsKDIlcGLWNHv4Yc9ujXgF99YQouP3ai5Tevwc/itW14dfkm13IeMs5dnKylVJuyTz7zlZktAICZE51z9ZYazQKa49f01TgDJcxzWaNlSnHuMn1gfsTcRwhxoyajZUpNCgyIxfadjaCShcM4xGgZO/yUQx0/1Frs3wwjZFIFWigyvRSw9dyD+tRh9in74OQDnHO2hoHs0TWWstxdfBxelns+Q66zDBUldevDoOhi1Xm1jw827MSVehw4AKza1I4DRw0IfO0oJmDmx4ofdLtC9YqWOf3mlz3P7xXGW/RhudsNjQNGDjAijuZ/7wQ8/OZaXP+EVpPIDy3TW2CGVc4kv4vHGLSMD85ddm3evuz6I0nce01Y7uLw/P31OzD7oUUlj/GyDKsRCulVGtetfZgyO3/jqdGtO7occ60CmlVTlGSoEhG+fvQEjBQiWsqBrHE3WDh357OVK0uyqB37afPZjOtH3CsFPwgY5B3by+G+bKN1/s9VW/xn6Irgfp9ylAVPuhIVlUO529pSb6Hou2KjbDYqMQqtHCf9kL71hqMV8IhzF5Z7CkWnI156DFmNKpdwSQ6vDywvozFMDwhIjmqvEeUudvLv/2Oxr2MctIzwWzXKdhqWu4967qW2AxpVA2hKXjopSVHuUI0apSx32YdHpsC0oTNzpQ3qchnXKeS6AlYc7O4tGp39o9adeHv1NvzwkcV46YNWacf2eob2mPDVW8KFS5olZEMdDgC44ZyD8PWjJ+DgsaZvplT29dm3vIK9v/ek9LfLjp1gWe8uOD8CFs69zLYmPnpXWkbYvueABt0RbxptrnHuEKkbJzI+lfsVJ0zCo5cfgf1H9HfdJy7UhHIXLTXfERSChQFoL3wfoTBQpZETOXcH6e59rOyDMKx/Ax69/Ahce+YBFs6dW+Sa5V55x4+sE1ppGVO28S4RHID4COTcaT6bwYpNu/CEJK69S6J03LCzqxd7fe+f+P2cZQCA4375Is76/Su4c+4qbG3vkcZor93qrrDtzsCObvlE4KVgJDGV8b6G9W/A7FP2sdEy2rsY2JTHgMa8gxZcvNY903bK6IGWddnHVYwi4dcd0rfeKCgWFqWSmPYb3h+fmjTUsNy94twB7zBb65m9P1K5bEY6/V8SUBPKXSyW5DclXeQG+Zb7Zh3uWoo2amQE5W4X2Y1zL9XPDxrVjMa6rJF1mssQLtMdaAXDci9P7lKQ9YO+QmanaLk/dMlMPPGfn3IeANPp5dYB63Qn7KWSuPYglnvrDi2i5f75zslNAC280Q4Z7dNfv8et7VbnI/84PLdkA259YblvudwmaCkXfFQ3ZXSzhXv2A/HdnrDvMBy79x7OffT2RWQq+pMPGIYfnLFfYFkt8+aW0O6HjhtkrBdFzt0lEoYBnvuIl0sQjR4INaHcrZa7v2NknPuAprxrKdqoIYtVt8sWFtxyr89ljBFCr+5QrdQMQhwyS1NM2xc7aXNTHfZzGc6KWYSARLl7fKU+9fM5uMql1LAdvChWubkNPz7rAAwf0IBt7daa+ly5X3znfNz45FLf5ysnickLnTqfvke/hpLVEe0Q38EN5xzo4O8B4NCWQfjyjLG48ZyDsFV/FhOG9rVUVpx79XF4+r+OchzruJ6w7FXPHRA+hvq60a8lzYS0HTwjXXx9WARUOOUiFGpCuff0mk/Wb6VA/rpMb3d1wS0cWShWKVlKNSRuHdfns8YIgTu/Kp19K+so/RuCB2UZH1+X30s5ve+bv9rzdw4e5lcn8QW44cCRTgMgmyE01WWxeZdVuYd17hqce8Qf443btZHKHv3rHYliYt+R9SM/9Zdy2QyuO/sAjBrYhM16nP/wAY2W6KXhAxoNH5EX/Dg1uRLOCSMd0Vcjdahyzh3u+/jl3DmM55ggK78mlLvYgRatbfN1jLPwT3XfipmhKrPcvRtyqc8XH3rXZU3LfYceb22fQDlqyDpC33r3ao9esEY92Dj3iHIRDOWeJd+VHR+9/AjcO2uGZVsuQ9hzQIPjwyuLKPGDfBm1ZbxwmE5fnLDvMIflLta2GX/NE45jRVH8lMXepY+K+jeGi7guFY4IOC33jKG43csC8Pv24tzF9hbkHfgJY64WakK5+4lrt8NBy0QpkA9wh5mWMW29upssfmXkVlJ9PmM0+jN/p9UJ7+NRnCkKyGimfiEtd2sntf7uJxeh1FRuC1ZtNWiUXDaDU37zL5+ykSMiKZfNYJQkbd6u3FtmP47rHnPWNP/TBdMt6zxDNWqbY+bEIVh2/SmYLOHc23u8nb9BS3Rw5zKfBjIo/NAyHFmL5V4iFFIfsXh+ACyWexCpk4OaSGIKpdwNh2o8GWZeFQ5KyVKKluHp/BOH9nUowUord5ml2lSXxYv/fUzJdHwRPBZZ7KTPfeto7NStSz8c5x9eWo4rTtAmMlmxaRf61ueM2Ok3Vm7BubfNNQpqfdS6U3oONyVmpyVyGcKogc5cAdnzuP3lFY6SsPZsz3Jry3jBpObI8hxLTVotNiU/PHS7/nEN83EHbAq2xPVEzl0sFO01E5NXGxKPq0ZodCWw21ruZiikvlrl9+fVYFyHdj5lHDO4CbecPw2//uIUx3Vkc6tGCVnoIBFh7OA+OHjsIP8nslmVRJpjjlc6FKOiDv/Zc9JT8HaxZVc3jv2fFzDrr/ON3zj3vEXnyO1RLgBwx0WH4N1rT5ae2+5MzGYI+wx3OofdOPdSitSgGSrYQ7U2bz7HUnXXxXYZhMbsF9Jy99PgeTswOXceCunlLLUq9lLlB5JYN8YPasJy7+71YcbZIGbSAdXnyjwbTASinKrPNGVX7k11lX3lduV+zjTnxBx+wPWO25sV6Z91bZ1Ys7XdsQ+Pr39Tnyhk4cfbjN/80Ap963PSiBDteDstQzikZYhjPzfO/YE3zdDLyaObHTfKr1tJxWLn3Esq95CicMv98mMnSjOUy7ked/yalj1ZJsh2OXNJv5WF7xf60CEtA/HGyq2O/VMdLUNEWSJaSESP6evjiGgeES0jovuIqE7fXq+vL9N/b6mQ7Aa8LPeWwU343b9NdWw3Q6ZsG6oET8u9FC0TIDrZYbnXV9ZyP3jsQEsy2C/1uWKDwp5p6HhBtkdw5I1zAGj3e9XJ+4BI+9Bs77TO28kYw/LWnb6mUpRNjMxhf325TMZS2pjDLZWfZ1IfOm4Q7rroUMc7jXqGLBmMKel0lBpNhBWFfwi/fdLeuPy4Sb6P83M5PvIWLXexcbhb7szVWW8/TnzXd391BhZfe5Jj/yn6iHK6kA0cN4IM+r4JYImwfiOAXzPGJgLYCuBiffvFALbq23+t71dReCn3By6ZaZnwgsNZOKy68Apxc3eoBpcyZxvXV5pz71ufs5RxjQr2x+WWiTz75H1wyTETUJfNoLu3iFvmLMfKzaZVf9WD7+D4X76IFyVzedrhZWXaL+8WPFIqWubfjxiHAU15TG8ZhBZhYmt+v5Wke4msz7GzRE0ZAgVKs//ZZw/EMS7z5fqBnw8b779GiWTifLpH+QH9r1fNd4t/wVabRzZj1BETh2D+907ACfsNKylzteBLuRPRKACnAfizvk4AjgPwgL7LnQDO1pfP0teh/348VTjO0N6BvnPy3sayrAohIKFlqsyreTmISskSZAjo5NzTwcRplAGzrItwS2fg1THrcxl09hSMycE5eCbqW6u3lZTBre2I1+Hgol539gGW7V1C3RoZ+Eiqf0MeL/z3scZ2fr8Vp2WE9S4ftMyDl8zEWz840df5zzt0jGPms0Dy+aFl7Jy77iSW1YziyJA20YtXRA1C+Bf4dIFJgV/L/SYA3wHAtehgANsYYzx2ag2AkfrySACrAUD/vU3f3wIimkVE84lofmur/9lrZBAtjgcvORyXHmPWrG5w4Uzt5QficJmMG9IHP/vsgb6TmM6dPgoN+QzOkIxE3GCnH5qbwjq3gmHmhMG44gT/Q3A7zOJO8t/dLPcB+v3V5bK4c+4qPO4yp6pYo5w7ae0YaJuazv7b3KuPw9Qx2rFcmi/PGIul152Mq0/ZB6ccsCe6C0XPOUTdRlKG5V5B050C0jIZIjTks5aJzCuJIMo9IzpUIShut8Jhgj+npOVeq9EyRHQ6gI2MsQVRXpgx9kfG2HTG2PShQ8MP3QBgZ5ep3OuyVmvLLSNTVn6g2pjz7WNw3qFjHNvdZJkwtC+WXncKxgx2xlO7QWyYfeqyntZolLjnazOMMMQwMMLVXGKR3ZR+s2C52/Hzcw4ylkWq5vQDh+P5bx1tyaRtbsp7TtgMaJmWYgE4joZ8Fv9x9AQ05rPo7i160oZu8fr8fiutV5hHtMxVJ1vnMq12H/FDQ/JHmxNDIcV2IzuvYTi4B1OI7S2lut2X5X4EgDOJaCWAe6HRMb8B0ExEvPWPArBWX14LYDQA6L8PALA5QpkdECvxyWb18UJc0TIi7FeOUhbOF/arz+HVq4+P7LyVhpFo4jKyOne6PAqHW+72GZBu+9LBlvrgIhrqshg/tC8euvQIHNqihWu2DHavWGmXE5BnGtflNN7fi0VrbpRbwVWhZWy8jF25f8FWybHqPSSA5W5JYoJ3iLORQwH3fSzlB1Kq3Usqd8bY1YyxUYyxFgBfBPA8Y+x8AHMAfE7f7UIAj+jLj+rr0H9/nnmRjhFgp5A27SctGhA5d+t6HLA/nChl4Y1+5MBGB0+cZNjD9OzPZP8RA6RlZLmyFFPpAeDkA/Z0Ve48ZHLiHn1xoT7Vm9/EG9M55wSf51W8j89OHWks3/SFKe6jMMOhWlnlLsr9o//Toop+/rmD8L8XHyad2KWa8HPv5gTVZN3m5UsrQflpu4iWe40qdw9cBeBKIloGjVO/Xd9+O4DB+vYrAcwuT8TS2ClY7m5xyXbYZy9P5+vzj9TxhgZt5r6LLEHI6wPmprDFevO8PZSiZDi++qnxACAthGUoT0s2lrk4xYXr13bTaZlKcu4g6YjjnGmjcOSkIRLlXjFRpPBzOTNaxuTceX6Em7wEWCqHqfIDABhjLwB4QV/+CIDDFc4Y6wRwbgSy+cauEMqdv6+it8u8KnDQMhHK0mtr/GmCxekleUF2R+UVJ0ySxppzuFUyFEMeeVvya7mfuN8wY95PO8zhP7Ns42iS5BwM61+PoyYNrVoo5D/e+gQn7rcnTj1wT2ObW0XKaluwvi6nP1r+EcwY+RHelSQ13e7Ny3Ok1XJPR1xcCVgsd70D33HRIdi0w72WCf9aF3X9kKRqblHKYrds0gL+DLwc3vbQPS9LGHBGDsnA21LYSpYiZBE/4muQhaXOu+YEADBqv1c6FBIALrvnTSz5sVZm4TsnmU5Ue+mDarcgP/2AP1r+XHmCkja5udt5bUlMUs49/bRM7Sl33XKXzRIjgr+uQozRMoYstotHKQsfmVRj0u8owTupF+yhe6U+YKI/piGfMY4Xp4s7fIIWtXv65OGB5JXBjNwQtgkiNnpELvHdKtkuxXbXrk8HKJaEtifAVZ2W8XE9e7IS9xF70jJkYWVcY+E50mYYcaSrx7tA9PL7VWL2UMgkfZ2jlGTq6GZ8acYY/CpkGYC4IHZSN/DU/n46P76ryzsJRwyLfesHnzYmfBZj//cfMQArbzgN08YMDCW3CHPiCDkt453Ipv2t5MxZ4pk79D4k0lp28apOy/jYx2m5myV/XaerhDWLVca5i9+1lOr22lDuRQYcMLI//v71wwMod+5Q5euVkq40Jg3ra1mPMiohl83gJ2cfiFED/cfGJwFG5T6P98Mt7wtmjgUA7C3UtPnDlw/GsbbUd7FYWC5DuOKEvXDHRYdgxnhHjl1ksH+g/FZ5NByqFTXdzUU++hVHE9WOjrHDz/XN76bdcmceljuv587XnfuIo7y4n0NY1IRyZ4yhubEOh7QEKCmrIwnRMjefN9Wyns6mFC34HKpeEyrwEdsZk0dgxc9OxbghZmz6Sfvvia8dNd6yf17QrNkMIZ/NlKTvyoERuSFo9ytP3Nt1f8exqGzJX/Gr87Ge1OU1U1ciaRnwkTc/CEZpAa9omVITelR6OspqoCY4dy9+zQsZMkuGxvlx7teQx2HjBmHeii2xy5I0eHVAHi1Tn8tKlb+dMxZpkGpYYwRrVMZ1Zx+Aof3q8cK3j7GUP5Afq/+toJw9RdNn8dGmXQDgGW2USFqGR8sYGb2kf1CZ57wIYharrMOVO2F6EpBq5f7Uu+vxwII1vifFtoOIhAJUcQ9BxeX0N6xyYdAywrodN583FX94aTlGS2ZAAtwrNVYLhlPY9oFqGeIj+5Xk4YhRQpxYfkWrrty9nLxVt9wDRMvo71obLJWy3LnDzTzGjnxFh0zVQarvYO3WDjzz3ga0dfSEUoiE+KbZU/CGPaJBhsmjm3HL+Qe7DqGzMXdQu1M4SBurRrRMr2AUrdAtd6/JXKodLhwkzp33f7EomHucO0qXH0irF1VAqpU7j3LY2t4TqtkRxVsVUgTvOOojw2Gf6Dj4g6mk1esHhqKxUQd+jwUq2y57BVpmw45OAN4ZvtXWd/50uz0Ukpf89Y5zL5bZttKAmlDumuUe/Hgx/TpuKqQanTlN0J6H6FANfo64R9Y8KiOM09782FeSljGV+9qtHchlyLX+jiZLxURxuV6Aj6FxjFDy1+XwjP5eIBxTi0i1ch8gVNQL5eyhZETLAIJyr9WWFhA8okFcDwq7Q7XaMKIy+HoQWoasfyuBHoGW6S0yDOvfUCJhJ3m0jGxUVKqiiEndhPPVpQWpVu5i8kmYZpchYZq9hOjUhIgRO8wks/DniNuhalYf5AZECEu0gi2i11Z4bfiABs/9k0nLaBCTmIo6LePGm9vLQtRqn4u7+ZeFZoEfDE/LmMtxQnHuTlgcqiGeS/wOVR6WZ2zwf2wVLHd7kFlTiUqY1R5V+ktistKqGU7nMa/HTaFHVGlCqpW7dYqyENEylJxoGZNzr9GWFhCGP6SMyVQS4VC1VIQMcqyorKqDUpdKh+VuUi5uHwfDn5OAiXoqiVQrd2tseIjj4T7RcmyozXYWGHzoLK4HRTbmRBSDc7eF6wVBNROHSinvJIZCGpy7OEE2eMlfl/PC+l5qtc+lWrmLDT9cKCQlxnLnSIgYscPujAyDZFju3nXDS58kSolKXEryvH5w+n7C79WTBQgYOsr/Ei/5K2617Ws3HMIKmHCkWrmLLyWs5Z4Yzl1Mn1YQKirq6yHOEXsoJGz3ENIvVC3ILN2zpowwZUlw0zSSmMAVt0fhMMd7SfCNlYFUK3er5R5OuyclWka0PBQ0WCJNQjyY2EMhyeoUDlv/qHpwXszSx6ruUPW/b0boQGbJX/fzlj2iSgFSXVumXM49kyBaRiUxeSPMc5HRMq/OPs74oFcaJrcb3HHnNQNVpVAqDb/abTPI8xIn6+BVIV3LD8DuCylT0IQi5cpdtCrCHC+WH0jGG67VIWJQkD6+LotzlzhURzTLi4xVBPq7DGO5x9EuZaMEcVsi51DVYVaF1NaLrEQ99xK1ZWoBqaZlgPJCCLUveEIsd9vf3R1i1AMQMlomboeq/jdMIlYcVqWsD2XKNKDKQbiMXjOG3evwsMllaUL6lbtjIcCxIi0TmUThQIqXscBRuS/Eg4ndoaqLHMZv4DVJSdTI6eau7HllY6RlgowUrHOo8pK/7tEy4qjQ7TKTRw0w5mROI1JNywAmbx4qFBJAISG8m7LcreBRD6yMcJnYHaq60GEqj1YzNT6jazvZB9Tq16oyLRNkXzL/cj+HZ7QMSo+oHrn8yAASJA+pV+7lFNwSM1STolYV566Bd1JxPSjiLsnNZQ7jtK8qXehhWaSFluFy9q3PYdHa7di4o8uDc+fPNxkVYSuF9I45dIjxrWGOTcI0e+L145YjKYiiw8XdafnHhUfnBKEZyqGjgoJfQSaf6Leofg5GEFpG+3vG5BHYtLMLyzbutMw0Zd/XWs+9NlFSuRNRAxG9TkRvE9G7RHStvn0cEc0jomVEdB8R1enb6/X1ZfrvLZW8AcPoCMO5IzmTdXAJ4pcjOTDqciOdz4V/XML4dcxSthELJUHGw0Cy0DKVF8X12qX31XY+cb9hxraCC+/C6+yraBmgC8BxjLHJAKYAOJmIZgC4EcCvGWMTAWwFcLG+/8UAturbf63vVzF4NcxSEGmZuK28cuilWoQWyVS+Y/HE/Ybhd/82NULJgqMYwq9TziQlQcGvIfuQlBtuXA6CXI7L3q8hj2vP3B8AMHFoX9fzljvLVxpQknNnGjm1U1/N6/8YgOMA/Ju+/U4APwJwK4Cz9GUAeADA74iIGAsTEFYaZXHuoMRZhkmRI3ZQNPU//nTB9CikCQU75x7kLswPQuVaxMOXzgQDcMHtr/u6VrUNjzDRMgBw4cwW9K3P4ai9hsp3JluGao12Ol8OVSLKAlgAYCKA3wNYDmAbY6xX32UNgJH68kgAqwGAMdZLRG0ABgPYZDvnLACzAGDMmDGhbyA6yz20CJGgHHqpFuGohZ5CcIswjHO0Gvc9dcxAAP4jteJyqPq5rn2fcw4e5b6vrW3Vapfz5VBljBUYY1MAjAJwKIB9yr0wY+yPjLHpjLHpQ4e6fGF9gBwLwY41+dBk0DK129SCQav/kW5e1LTc9fUAx9onfq4ofI5+q+9O9W+4BZ18vNan2AMCRsswxrYBmAPgcADNRMQt/1EA1urLawGMBgD99wEANkchrAxlZagSheJDK4mkyBE3aoEX5RKbEVkBomXicKiWuFZc5Qf8PLdAzlfsHrVl/ETLDCWiZn25EcCJAJZAU/Kf03e7EMAj+vKj+jr035+vFN+uy6T/DXNsxMKUgSBWyu4A+7tJ0rvyi3Is92qG6Ho5VGX7VRt+PnDBLXfRek9h4/IBP5z7cAB36rx7BsD9jLHHiOg9APcS0U8ALARwu77/7QD+SkTLAGwB8MUKyG2gnKx9a/ZdJOKEhopzd8LaAdOHsjh32zkqCZNzL0XLVNmhKsyuVHLfIMod5dfZTwP8RMu8A8ARS8YY+wga/27f3gng3Eik8wGzGly4aBljOSmhkDVqRQSFvQOmEQ7LPYRDtRrN0uhDJcbxsYVC+rLcA5w3okispCP9Gar8b5m0TFJecK1aEUHh6IApfi5hnPbVLBzm15kfW7SMr50DnBflz22bBqRfuZfDuVvOE408YaE4dyt4BzTX0/dk7BmqQW6humF6/vpQ1SfI9ikXEHDkru9b6zMx1YByN5ZCHCvQMnG/4gCRAbsFeAdMMS/D32QYRV3NwmGc0ihFbVS7EJvp6I2ac9dQ65x76pV7xlCKwY9NlEM13ssnDrXQAe0ZqsHquevniFgmGfz6e+Iq+evnqkE5dyA5CYyVQuqVezl0Brksx4labWhhkZzCbsHBZS6nnnumCuYy70PJs9zJ8tfPvr7OCytdFvuovUJIvXIvz3JPjke1HN9BLYI/h2o6FqOGoypkgFuo5gxhZh9KmOUewKEaznLnG4JIlR6kXrmb9dzDhEKaqH6taivMIWiNtrSAKGcWo6TAUCJl1HOvxtc+qR/OIO60IPeQsb2XZN59+agB5W79G+ZYIP4XrJKYrDCeQw04VJM+zR5H3AaOHUEm4glmudsMh4Tdd1SoHeUe5tgEJTFxJEOK+OFQjGl8MGWEQqKKhcN48lJSn7Efv0OY58SqSH3FgdQrd7PoUZhQSGE5KoFCwqBlktrDqoxyIk2SAvMDFdxxVyzq56jCbft1qFYbQZRviDD3dBsOPpB65V7Oe4lzlhk7ypkLthbBn0d6SRnBKVzGTEzVULhBqi9WE2a552gtdxUtkxL4LVcqgzUUMhkO1RptZ6GRYspdokT8w+Tpq0DLJNSwCMJmBa0KCag49+TD4NzLo2WS0rITIkZiwBhLbedzDv8DRMtUkTJIKiUYZBq8IJLbffXJuuvokHrlbjbMEMeSfDkWJHRoHBdE6yqtT8TBuQehZarpa0hopFZTnVa09oR9h5XcNxznXtva3dccqklGWXOoitEyEckTFqpwmBVinHtaP3gm5x6clqlmfo1ZNrsKFwuAvvU5zL36OAzpW19y33AZqtb1WkPqlXtkce5xJzEl1HqKC3ZnZBrhUCKhLPeopXLCGP0mUMkNH9AY+TntH91aReppmfJCIZNjuXMksYPFAZHSSO0TsQ//A9wJP6Iace67q2GhQiFTgnC0jLAcdygk/1ujDS0sUu1Q1f+GUSLVVDzlGEhpBL/Pd9Zs09ZjlKWSSL1yNyybEG9I5Bjjtph3k37lG2bhsPjfTVgYsfphOPcYKIN0PuXgaG7MAwBueWE5gNr9qNUO5x6iaTbWZR3niQvmrDO12dCCwpxcOmZByoBhuReDR77sNawfgHUV4Zzd4EYBPXzpTLyzpq1qclQaR+011LJeq10u9cq9nCQmHmqVJNRoOwsMS7haSh8Kv4dCiIi7y46diCMmDsbBYwdFLpcb3PrQ1DEDMXXMwKrJUWkM7VePDKW74qgfpJ6WKadwWJ8kWe67qVOrFIrp1e2G4WFa7v6PzWaoqoodSO9zDoOiMCKs1T5XA8q9DMu93rTc4+Z1lXK3QuSr0/pM7MkySSupa0c1Zn1KCsYN6SOs1eZ9p1+563/DdJymfHIsd464PzJJAX8KGiuT7mdSTLHfIOk4a8qIUMfdO2uGsZyUvh81Uq/cM2XQMlbLPV5wx2FPoRivIAkDS3FdyHKm2YsDSR9ZyPCbL07FyhtOC3zcsP4N2Hd4fwDx9/1KoaRyJ6LRRDSHiN4joneJ6Jv69kFE9AwRfaj/HahvJyL6LREtI6J3iGhaJW+AyuAzrJx7vK94/fZOAMDS9TtilSMpEItupVDnABBHH+koLZvW5xwWdTlN/dXqyMqP5d4L4FuMsf0AzABwGRHtB2A2gOcYY5MAPKevA8ApACbp/2YBuDVyqQWQ7W8QJMly//rRE2KWIFmohQzVtE0KkXDxIke9rty7e2tztFxSuTPG1jHG3tSXdwBYAmAkgLMA3KnvdieAs/XlswDcxTS8BqCZiIZHLThHOY7IfqJyj7llHzByQLwCJAymQzX+UVVYOOq5J/w20kjLlAOu3Lt6CzFLUhkE4tyJqAXAVADzAAxjjK3Tf1oPgNflHAlgtXDYGn2b/VyziGg+Ec1vbW0NKrdTthB2x7H77GEo+LgViEgRKdRGcSeH5Z5w23g30+2oz2l9rlYtd99ZPETUF8CDAK5gjG0XlSFjjBFRoF7IGPsjgD8CwPTp08vuwWEa5oDGPN7+4aexraOn3MuXjbg/LkmDWJclrU/Gwbkn/EZ2tzZoWu61qdx9We5ElIem2O9mjD2kb97A6Rb970Z9+1oAo4XDR+nbKoJy66BnMoRBfeqiE0ghGoiRJinVOY7p3GKUxQ+SLl/UGNpPqxOfq9H4fj/RMgTgdgBLGGO/En56FMCF+vKFAB4Rtl+gR83MANAm0DcVw25mdOw20AqHpRX8A6WvJfxGki5f1Ljq5H3w/dP3w0n77xm3KBWBH1rmCABfBrCIiN7St10D4AYA9xPRxQBWAfi8/tsTAE4FsAxAO4CLohTYAcOhupu1zBqH8TbTS7k7p3NL+Gdqd3OoNtZlcfGR4+IWo2IoqdwZYy/DvVUeL9mfAbisTLl2S/zvxYelOmknSljmUE2p0hGzbIHkW8ZJl08hGJJXFjEgjDj3GmiYR04aErcIiYEYRpjWd2tkqBaTzbmbH5+kSqgQBqkvP8CR9DAzhWCwZKjGK0poiBE/QPKVZ7KlUwiK1Ct3VU2xNmGnNNKItETLqD5Um0i9cudQ7bJWkWLO3a7cE3ob/AO6uzlUax2pV+7m9HQxC6IQKWqDlrGWH0i68ky2dApBkXrlzqE499pCLThUIXyg0oCkf3wUgiH1yl3xhTUKo7ZMvGKUg7SVH1D2UW0h9cpdoTYhlvxNq9YxQyGt60lFsqVTCIrUK3fTcldNs5ZgLfkbszAhYf1AJV95KlqmtpB65c6hmmVtQU3WUX0kXT6FYKgd5a4aZk0i3ZY7H32kY5o9ZbnXFlKv3Mst+auQTBiTdaS41k5a4tw5ki6fQjCkXrlzZGq0JvPuCmucezrfraP8QGySKOyOqBnlrjpObUGkNFJrUdpL/ib8PhQtU1uoGeWeXg2gIEMtZaiauj3Zd6K6UG0h9crdCIWMVwyFCqE2JshONufO/RrKcq8tpF65c6h2WZsosvTmMKQtzj3p8ikEQ+0od9U0awp1Wa1pdqd4Znr+USokPENVFd+rTdSOclcNs6bQvzEPANje2ZPad8sDuFjCLXdOyyT146MQDqlX7tmMinOvRfRv0JR7W0dPzJKEB9eVhYRz7hwJF08hIFKv3HMZ7RaS3nEUgqF/oza9747O3hS/W162WF9L6I0M0EdJSZVPIRxqQLlzy101zFoCt9yB9L5bstMyCb2Nm744FecfNgZTxzTHLYpChMjFLUC5yGVVLGQtol+D2TSTqhRLIS3RMiObG3H9Zw6MWwyFiJF6yz2vR1UkteMohEMuazbNVZvbY5QkPNJWz12htpB65c5pmWKKk10UahNpsdwVahMllTsR/YWINhLRYmHbICJ6hog+1P8O1LcTEf2WiJYR0TtENK2SwgMmLdOblokqFQLj2SuPiluEUDA5d+u6gkI14Mdy/38ATrZtmw3gOcbYJADP6esAcAqASfq/WQBujUZMd/Bomd6CUu61hkuOmQAAGDu4T8yShIM4ybe4rqBQDZRU7oyxlwBssW0+C8Cd+vKdAM4Wtt/FNLwGoJmIhkckqxTccu8ppDeTUUGO75y0Nz74ySmGXyVtSEttGYXaRNheM4wxtk5fXg9gmL48EsBqYb81+jYHiGgWEc0novmtra0hxTAdqgVFy9QciAh1uXQqdhGqaSrEgbJ7DtOCeAM3X8bYHxlj0xlj04cOHRr6+jxDVXHuCkmDkaGqt82smlBGoYoIq9w3cLpF/7tR374WwGhhv1H6toohn1G0jEIywTn2RWvbUJfNpJZeUkgnwra2RwFcqC9fCOARYfsFetTMDABtAn1TEfB4aOVQVUgaRI69T302PkEUdkuUzFAlor8BOAbAECJaA+CHAG4AcD8RXQxgFYDP67s/AeBUAMsAtAO4qAIyW6BCIRWSCrFccVNd6pPBFVKGki2OMXaey0/HS/ZlAC4rV6gg4ElMvYqWUUgYmupMa72zpxCjJAq7I1JPAhpx7spyV0gYJg3rh2vP3B8AsLW9O2ZpFHY3pF+5qzh3hQRj5oTBAFQ4pEL1kXrlzsPLVJy7QhIxcmBj3CIo7KZIvXJvzGu8poohVkgiuCN13JB0llBQSC9S78I/Y/IIfLBhp1GHREEhaXjh28eguSlfekcFhQiReuWez2Yw+5R94hZDQcEVLcpqV4gBqadlFBQUFBScUMpdQUFBoQahlLuCgoJCDUIpdwUFBYUahFLuCgoKCjUIpdwVFBQUahBKuSsoKCjUIJRyV1BQUKhBEGPx12QholZodeHDYAiATRGKU0mkRVYlZ/RIi6xpkRNIj6yVlHMsY0w6T2kilHs5IKL5jLHpccvhB2mRVckZPdIia1rkBNIja1xyKlpGQUFBoQahlLuCgoJCDaIWlPsf4xYgANIiq5IzeqRF1rTICaRH1ljkTD3nrqCgoKDgRC1Y7goKCgoKNijlrqCgoFCDSLVyJ6KTieh9IlpGRLNjluUvRLSRiBYL2wYR0TNE9KH+d6C+nYjot7rc7xDRtCrKOZqI5hDRe0T0LhF9M8GyNhDR60T0ti7rtfr2cUQ0T5fpPiKq07fX6+vL9N9bqiWrfv0sES0koscSLudKIlpERG8R0Xx9WxLffzMRPUBES4loCREdnjQ5iWhv/Tnyf9uJ6IpEyMkYS+U/AFkAywGMB1AH4G0A+8Uoz1EApgFYLGz7OYDZ+vJsADfqy6cC+CcAAjADwLwqyjkcwDR9uR+ADwDsl1BZCUBffTkPYJ4uw/0Avqhvvw3AJfrypQBu05e/COC+KreBKwHcA+AxfT2pcq4EMMS2LYnv/04AX9WX6wA0J1FOQd4sgPUAxiZBzqrefMQP8nAATwnrVwO4OmaZWmzK/X0Aw/Xl4QDe15f/AOA82X4xyPwIgBOTLiuAJgBvAjgMWrZfzt4OADwF4HB9OafvR1WSbxSA5wAcB+AxvfMmTk79mjLlnqj3D2AAgBX255I0OW2yfRrAK0mRM820zEgAq4X1Nfq2JGEYY2ydvrwewDB9ORGy63TAVGgWcSJl1amOtwBsBPAMtNHaNsZYr0QeQ1b99zYAg6sk6k0AvgOgqK8PTqicAMAAPE1EC4holr4tae9/HIBWAHfoVNefiahPAuUU8UUAf9OXY5czzco9VWDaZzoxcadE1BfAgwCuYIxtF39LkqyMsQJjbAo0y/hQAImbDZ2ITgewkTG2IG5ZfOJIxtg0AKcAuIyIjhJ/TMj7z0GjOW9ljE0FsAsavWEgIXICAHR/ypkA/m7/LS4506zc1wIYLayP0rclCRuIaDgA6H836ttjlZ2I8tAU+92MsYeSLCsHY2wbgDnQ6I1mIspJ5DFk1X8fAGBzFcQ7AsCZRLQSwL3QqJnfJFBOAABjbK3+dyOAh6F9NJP2/tcAWMMYm6evPwBN2SdNTo5TALzJGNugr8cuZ5qV+xsAJukRCXXQhkSPxiyTHY8CuFBfvhAav823X6B7zmcAaBOGcBUFERGA2wEsYYz9KuGyDiWiZn25EZpvYAk0Jf85F1n5PXwOwPO61VRRMMauZoyNYoy1QGuHzzPGzk+anABARH2IqB9fhsYTL0bC3j9jbD2A1US0t77peADvJU1OAefBpGS4PPHKWU2HQwUcGKdCi/ZYDuC7McvyNwDrAPRAszouhsajPgfgQwDPAhik70sAfq/LvQjA9CrKeSS0IeI7AN7S/52aUFkPArBQl3UxgB/o28cDeB3AMmjD4Hp9e4O+vkz/fXwM7eAYmNEyiZNTl+lt/d+7vN8k9P1PATBff///ADAwoXL2gTbyGiBsi11OVX5AQUFBoQaRZlpGQUFBQcEFSrkrKCgo1CCUcldQUFCoQSjlrqCgoFCDUMpdQUFBoQahlLuCgoJCDUIpdwUFBYUaxP8HxbKDi2eDItIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_data['num'].plot.line()\n",
    "\n",
    "test_data[test_data['num'] >= np.percentile(train_data['num'], 99)].reset_index().plot.scatter(x=0,y='num')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
